The consistency and precision with which singing humpback whales interleaved broadband and reverberant CF elements of units confirm two novel predictions of the duplex sonar model.Metamaterials are attracting increasing interest in the field of acoustics due to their sound insulation effects. By periodically arranged structures, acoustic metamaterials can influence the way sound propagates in acoustic media. To date, the design of acoustic metamaterials relies primarily on the expertise of specialists since most effects are based on localized solutions and interference. This paper outlines a deep learning-based approach to extend current knowledge of metamaterial design in acoustics. We develop a design method by using conditional generative adversarial networks. The generative network proposes a cell candidate regarding a desired transmission behavior of the metamaterial. To validate our method, numerical simulations with the finite element method are performed. Our study reveals considerable insight into design strategies for sound insulation tasks. By providing design directives for acoustic metamaterials, cell candidates can be inspected and tailored to achieve desirable transmission characteristics.The convex sparse penalty based compressive beamforming technique can achieve robust high resolution in direction-of-arrival (DOA) estimation tasks, but it often leads to an insufficient sparsity-inducing problem due to its convex loose approximation to ideal ?0 nonconvex penalty. On the contrary, the nonconvex sparse penalty can tightly approximate ?0 penalty to effectively enhance DOA estimation accuracy, but it incurs an initialization sensitivity problem due to its multiple local minimas. Leveraging their individual advantages, a minimax-concave penalty (MCP) regularized DOA estimation algorithm is proposed to achieve a maximally sparse level while maintaining the convex property of the overall objective function. Moreover, an accelerated block gradient descent-ascent algorithm with convergence guarantee is developed to rapidly achieve its one optimal point. Simulation results demonstrate that MCP penalty improves DOA estimation accuracy compared with popular sparse compressive beamforming techniques in strong noise scenarios and weak source confirmation. Ocean experimental results also validate that it retains more stable DOA estimation accuracy and incurs less artificial interferences.A smeared spectrogram is a result of the smoothing kernel in the short-time Fourier-transform (STFT). Besides the smeared energy, time and frequency phase information is also smeared, i.e., spectral components may contain imprecise phase information. The STFT is also used as the basis for more advanced signal processing techniques such as frequency-domain beamforming and cross correlation (CC). Both methods seek the delay time between signals by exploring phase-shifts in the frequency domain. Due to the inexact phase information in some of the time-frequency elements, their phase shifts are incorrect. This study re-introduces the reassigned spectrogram (RS) as a measure to fix the STFT artifacts. Moreover, it is shown that by using the RS, phase shifts can be optimized and improve beamforming and CC results. Synthetic and recorded data are used to show the advantage of using the RS in time-frequency analysis, CC, and beamforming. Results show that, subject to certain constraints, the RS provides exact time-frequency representation of deterministic signals and significantly improve CC and beamforming results. Array analysis of infrasonic signals shows that better results are obtained by either the RS- or STFT-based analysis depending on the signals' spectral components and noise levels.The goal of the present investigation is to study the effect of using fluid inserts for noise control at high exhaust temperatures by performing a sequence of large eddy simulations on a typical military-style nozzle, both with and without fluid inserts, at jet inlet total temperature ratios of 2.5, 5, and 7. An exact physics-based splitting of the jet flow-field into its hydrodynamic, acoustic, and thermal components reveals clear evidence of a reduction in the radiation efficiency of Mach waves from the controlled jet. This effect is far more pronounced at afterburner conditions, where the location of the maximum noise reduction is observed to shift upstream with increase in jet temperature, thus matching the maximum location of the jet OASPL directivity. Moreover, the maximum noise reduction achieved at afterburner conditions exceeds that obtained at lower exhaust temperatures. This is encouraging and shows that the effectiveness of the fluid inserts improves with an increase in jet exhaust temperature. Furthermore, by accounting for the effect of bleeding off bypass air for the fluid inserts in the LES simulation, this noise reduction is predicted to be achieved at a conservative thrust loss estimate of under 2% at both laboratory and afterburner operating conditions.Probabilistic models to quantify context effects in speech recognition have proven their value in audiology. Boothroyd and Nittrouer [J. Acoust. Soc. Am. 84, 101-114 (1988)] introduced a model with the j-factor and k-factor as context parameters. Later, Bronkhorst, Bosman, and Smoorenburg [J. Acoust. https://www.selleckchem.com/products/ki20227.html Soc. Am. 93, 499-509 (1993)] proposed an elaborated mathematical model to quantify context effects. The present study explores existing models and proposes a new model to quantify the effect of context in sentence recognition. The effect of context is modeled by parameters that represent the change in the probability that a certain number of words in a sentence are correctly recognized. Data from two studies using a Dutch sentence-in-noise test were analyzed. The most accurate fit was obtained when using signal-to-noise ratio-dependent context parameters. Furthermore, reducing the number of context parameters from five to one had only a small effect on the goodness of fit for the present context model. An analysis of the relationships between context parameters from the different models showed that for a change in word recognition probability, the different context parameters can change in opposite directions, suggesting opposite effects of sentence context. This demonstrates the importance of controlling for the recognition probability of words in isolation when comparing the use of sentence context between different groups of listeners.